OpenRaiser/Intern-Atlas huggingface dataset:
Qurts: Automatic Quantum Uncomputation by Affine Types with Lifetime
Venue: arXiv cs.PL 2024
Year: 2024

Uncomputation is a feature in quantum programming that allows the programmer to discard a value without losing quantum information, and that allows the compiler to reuse resources. Whereas quantum information has to be treated linearly by the type system, automatic uncomputation enables the programmer to treat it affinely to some extent. Automatic uncomputation requires a substructural type system between linear and affine, a subtlety that has only been captured by existing languages in an ad hoc way. We extend the Rust type system to the quantum setting to give a uniform framework for automatic uncomputation called Qurts (pronounced quartz). Specifically, we parameterise types by lifetimes, permitting them to be affine during their lifetime, while being restricted to linear use outside their lifetime. We also provide two operational semantics: one based on classical simulation, and one that does not depend on any specific uncomputation strategy.
CODECLEANER: Elevating Standards with A Robust Data Contamination Mitigation Toolkit
Venue: arXiv cs.SE 2024
Year: 2024

Data contamination presents a critical barrier preventing widespread industrial adoption of advanced software engineering techniques that leverage code language models (CLMs). This phenomenon occurs when evaluation data inadvertently overlaps with the public code repositories used to train CLMs, severely undermining the credibility of performance evaluations. For software companies considering the integration of CLM-based techniques into their development pipeline, this uncertainty about true performance metrics poses an unacceptable business risk. Code refactoring, which comprises code restructuring and variable renaming, has emerged as a promising measure to mitigate data contamination. It provides a practical alternative to the resource-intensive process of building contamination-free evaluation datasets, which would require companies to collect, clean, and label code created after the CLMs' training cutoff dates. However, the lack of automated code refactoring tools and scientifically validated refactoring techniques has hampered widespread industrial implementation. To bridge the gap, this paper presents the first systematic study to examine the efficacy of code refactoring operators at multiple scales (method-level, class-level, and cross-class level) and in different programming languages. In particular, we develop an open-sourced toolkit, CODECLEANER, which includes 11 operators for Python, with nine method-level, one class-level, and one cross-class-level operator. A drop of 65% overlap ratio is found when applying all operators in CODECLEANER, demonstrating their effectiveness in addressing data contamination. Additionally, we migrate four operators to Java, showing their generalizability to another language. We make CODECLEANER online available to facilitate further studies on mitigating CLM data contamination.
Automatic Discovery and Assessment of Interpretable Systematic Errors in Semantic Segmentation
Venue: arXiv cs.CV 2024
Year: 2024

This paper presents a novel method for discovering systematic errors in segmentation models. For instance, a systematic error in the segmentation model can be a sufficiently large number of misclassifications from the model as a parking meter for a target class of pedestrians. With the rapid deployment of these models in critical applications such as autonomous driving, it is vital to detect and interpret these systematic errors. However, the key challenge is automatically discovering such failures on unlabelled data and forming interpretable semantic sub-groups for intervention. For this, we leverage multimodal foundation models to retrieve errors and use conceptual linkage along with erroneous nature to study the systematic nature of these errors. We demonstrate that such errors are present in SOTA segmentation models (UperNet ConvNeXt and UperNet Swin) trained on the Berkeley Deep Drive and benchmark the approach qualitatively and quantitatively, showing its effectiveness by discovering coherent systematic errors for these models. Our work opens up the avenue to model analysis and intervention that have so far been underexplored in semantic segmentation.
Evolution of IVR building techniques: from code writing to AI-powered automation
Venue: arXiv cs.SE 2024
Year: 2024

Interactive Voice Response (IVR) systems have undergone significant transformation in recent years, moving from traditional code-based development to more user-friendly approaches leveraging widgets and, most recently, harnessing the power of Artificial Intelligence (AI) for automated IVR flow creation. This paper explores the evolution of IVR building techniques, highlighting the industry's revolution and shaping the future of IVR systems. The authors delve into the historical context, current trends, and future prospects of IVR development, elucidating the impact of AI on simplifying IVR creation processes and enhancing customer experiences.
Planning for Tabletop Object Rearrangement
Venue: arXiv cs.RO 2024
Year: 2024

Finding an high-quality solution for the tabletop object rearrangement planning is a challenging problem. Compared to determining a goal arrangement, rearrangement planning is challenging due to the dependencies between objects and the buffer capacity available to hold objects. Although orla* has proposed an A* based searching strategy with lazy evaluation for the high-quality solution, it is not scalable, with the success rate decreasing as the number of objects increases. To overcome this limitation, we propose an enhanced A*-based algorithm that improves state representation and employs incremental goal attempts with lazy evaluation at each iteration. This approach aims to enhance scalability while maintaining solution quality. Our evaluation demonstrates that our algorithm can provide superior solutions compared to orla*, in a shorter time, for both stationary and mobile robots.
Exciting Contact Modes in Differentiable Simulations for Robot Learning
Venue: arXiv cs.RO 2024
Year: 2024

In this paper, we explore an approach to actively plan and excite contact modes in differentiable simulators as a means to tighten the sim-to-real gap. We propose an optimal experimental design approach derived from information-theoretic methods to identify and search for information-rich contact modes through the use of contact-implicit optimization. We demonstrate our approach on a robot parameter estimation problem with unknown inertial and kinematic parameters which actively seeks contacts with a nearby surface. We show that our approach improves the identification of unknown parameter estimates over experimental runs by an estimate error reduction of at least $\sim 84\%$ when compared to a random sampling baseline, with significantly higher information gains.
Memory-Augmented Multimodal LLMs for Surgical VQA via Self-Contained Inquiry
Venue: arXiv cs.CV 2024
Year: 2024

Comprehensively understanding surgical scenes in Surgical Visual Question Answering (Surgical VQA) requires reasoning over multiple objects. Previous approaches address this task using cross-modal fusion strategies to enhance reasoning ability. However, these methods often struggle with limited scene understanding and question comprehension, and some rely on external resources (e.g., pre-extracted object features), which can introduce errors and generalize poorly across diverse surgical environments. To address these challenges, we propose SCAN, a simple yet effective memory-augmented framework that leverages Multimodal LLMs to improve surgical context comprehension via Self-Contained Inquiry. SCAN operates autonomously, generating two types of memory for context augmentation: Direct Memory (DM), which provides multiple candidates (or hints) to the final answer, and Indirect Memory (IM), which consists of self-contained question-hint pairs to capture broader scene context. DM directly assists in answering the question, while IM enhances understanding of the surgical scene beyond the immediate query. Reasoning over these object-aware memories enables the model to accurately interpret images and respond to questions. Extensive experiments on three publicly available Surgical VQA datasets demonstrate that SCAN achieves state-of-the-art performance, offering improved accuracy and robustness across various surgical scenarios.
Evaluating Generative AI Systems is a Social Science Measurement Challenge
Venue: arXiv cs.CY 2024
Year: 2024

Across academia, industry, and government, there is an increasing awareness that the measurement tasks involved in evaluating generative AI (GenAI) systems are especially difficult. We argue that these measurement tasks are highly reminiscent of measurement tasks found throughout the social sciences. With this in mind, we present a framework, grounded in measurement theory from the social sciences, for measuring concepts related to the capabilities, impacts, opportunities, and risks of GenAI systems. The framework distinguishes between four levels: the background concept, the systematized concept, the measurement instrument(s), and the instance-level measurements themselves. This four-level approach differs from the way measurement is typically done in ML, where researchers and practitioners appear to jump straight from background concepts to measurement instruments, with little to no explicit systematization in between. As well as surfacing assumptions, thereby making it easier to understand exactly what the resulting measurements do and do not mean, this framework has two important implications for evaluating evaluations: First, it can enable stakeholders from different worlds to participate in conceptual debates, broadening the expertise involved in evaluating GenAI systems. Second, it brings rigor to operational debates by offering a set of lenses for interrogating the validity of measurement instruments and their resulting measurements.
Anomaly Detection for People with Visual Impairments Using an Egocentric 360-Degree Camera
Venue: arXiv cs.CV 2024
Year: 2024

Recent advancements in computer vision have led to a renewed interest in developing assistive technologies for individuals with visual impairments. Although extensive research has been conducted in the field of computer vision-based assistive technologies, most of the focus has been on understanding contexts in images, rather than addressing their physical safety and security concerns. To address this challenge, we propose the first step towards detecting anomalous situations for visually impaired people by observing their entire surroundings using an egocentric 360-degree camera. We first introduce a novel egocentric 360-degree video dataset called VIEW360 (Visually Impaired Equipped with Wearable 360-degree camera), which contains abnormal activities that visually impaired individuals may encounter, such as shoulder surfing and pickpocketing. Furthermore, we propose a new architecture called the FDPN (Frame and Direction Prediction Network), which facilitates frame-level prediction of abnormal events and identifying of their directions. Finally, we evaluate our approach on our VIEW360 dataset and the publicly available UCF-Crime and Shanghaitech datasets, demonstrating state-of-the-art performance.
Avian-Inspired High-Precision Tracking Control for Aerial Manipulators
Venue: arXiv cs.RO 2024
Year: 2024

Aerial manipulators, composed of multirotors and robotic arms, have a structure and function highly reminiscent of avian species. This paper studies the tracking control problem for aerial manipulators. This paper studies the tracking control problem for aerial manipulators. We propose an avian-inspired aerial manipulation system, which includes an avian-inspired robotic arm design, a Recursive Newton-Euler (RNE) method-based nonlinear flight controller, and a coordinated controller with two modes. Compared to existing methods, our proposed approach offers several attractive features. First, the morphological characteristics of avian species are used to determine the size proportion of the multirotor and the robotic arm in the aerial manipulator. Second, the dynamic coupling of the aerial manipulator is addressed by the RNE-based flight controller and a dual-mode coordinated controller. Specifically, under our proposed algorithm, the aerial manipulator can stabilize the end-effector's pose, similar to avian head stabilization. The proposed approach is verified through three numerical experiments. The results show that even when the quadcopter is disturbed by different forces, the position error of the end-effector achieves millimeter-level accuracy, and the attitude error remains within 1 degree. The limitation of this work is not considering aggressive manipulation like that seen in birds. Addressing this through future studies that explore real-world experiments will be a key direction for research.
Framework for developing and evaluating ethical collaboration between expert and machine
Venue: arXiv cs.CV 2024
Year: 2024

Precision medicine is a promising approach for accessible disease diagnosis and personalized intervention planning in high-mortality diseases such as coronary artery disease (CAD), drug-resistant epilepsy (DRE), and chronic illnesses like Type 1 diabetes (T1D). By leveraging artificial intelligence (AI), precision medicine tailors diagnosis and treatment solutions to individual patients by explicitly modeling variance in pathophysiology. However, the adoption of AI in medical applications faces significant challenges, including poor generalizability across centers, demographics, and comorbidities, limited explainability in clinical terms, and a lack of trust in ethical decision-making. This paper proposes a framework to develop and ethically evaluate expert-guided multi-modal AI, addressing these challenges in AI integration within precision medicine. We illustrate this framework with case study on insulin management for T1D. To ensure ethical considerations and clinician engagement, we adopt a co-design approach where AI serves an assistive role, with final diagnoses or treatment plans emerging from collaboration between clinicians and AI.
Modulating Reservoir Dynamics via Reinforcement Learning for Efficient Robot Skill Synthesis
Venue: arXiv cs.RO 2024
Year: 2024

A random recurrent neural network, called a reservoir, can be used to learn robot movements conditioned on context inputs that encode task goals. The Learning is achieved by mapping the random dynamics of the reservoir modulated by context to desired trajectories via linear regression. This makes the reservoir computing (RC) approach computationally efficient as no iterative gradient descent learning is needed. In this work, we propose a novel RC-based Learning from Demonstration (LfD) framework that not only learns to generate the demonstrated movements but also allows online modulation of the reservoir dynamics to generate movement trajectories that are not covered by the initial demonstration set. This is made possible by using a Reinforcement Learning (RL) module that learns a policy to output context as its actions based on the robot state. Considering that the context dimension is typically low, learning with the RL module is very efficient. We show the validity of the proposed model with systematic experiments on a 2 degrees-of-freedom (DOF) simulated robot that is taught to reach targets, encoded as context, with and without obstacle avoidance constraint. The initial data set includes a set of reaching demonstrations which are learned by the reservoir system. To enable reaching out-of-distribution targets, the RL module is engaged in learning a policy to generate dynamic contexts so that the generated trajectory achieves the desired goal without any learning in the reservoir system. Overall, the proposed model uses an initial learned motor primitive set to efficiently generate diverse motor behaviors guided by the designed reward function. Thus the model can be used as a flexible and effective LfD system where the action repertoire can be extended without new data collection.
Spectral Subspace Clustering for Attributed Graphs
Venue: arXiv cs.SI 2024
Year: 2024

Subspace clustering seeks to identify subspaces that segment a set of n data points into k (k<<n) groups, which has emerged as a powerful tool for analyzing data from various domains, especially images and videos. Recently, several studies have demonstrated the great potential of subspace clustering models for partitioning vertices in attributed graphs, referred to as SCAG. However, these works either demand significant computational overhead for constructing the nxn self-expressive matrix, or fail to incorporate graph topology and attribute data into the subspace clustering framework effectively, and thus, compromise result quality. Motivated by this, this paper presents two effective and efficient algorithms, S2CAG and M-S2CAG, for SCAG computation. Particularly, S2CAG obtains superb performance through three major contributions. First, we formulate a new objective function for SCAG with a refined representation model for vertices and two non-trivial constraints. On top of that, an efficient linear-time optimization solver is developed based on our theoretically grounded problem transformation and well-thought-out adaptive strategy. We then conduct an in-depth analysis to disclose the theoretical connection of S2CAG to conductance minimization, which further inspires the design of M-S2CAG that maximizes the modularity. Our extensive experiments, comparing S2CAG and M-S2CAG against 17 competitors over 8 benchmark datasets, exhibit that our solutions outperform all baselines in terms of clustering quality measured against the ground truth while delivering high efficiency
Electrostatic Force Regularization for Neural Structured Pruning
Venue: arXiv cs.CV 2024
Year: 2024

The demand for deploying deep convolutional neural networks (DCNNs) on resource-constrained devices for real-time applications remains substantial. However, existing state-of-the-art structured pruning methods often involve intricate implementations, require modifications to the original network architectures, and necessitate an extensive fine-tuning phase. To overcome these challenges, we propose a novel method that, for the first time, incorporates the concepts of charge and electrostatic force from physics into the training process of DCNNs. The magnitude of this force is directly proportional to the product of the charges of the convolution filter and the source filter, and inversely proportional to the square of the distance between them. We applied this electrostatic-like force to the convolution filters, either attracting filters with opposite charges toward non-zero weights or repelling filters with like charges toward zero weights. Consequently, filters subject to repulsive forces have their weights reduced to zero, enabling their removal, while the attractive forces preserve filters with significant weights that retain information. Unlike conventional methods, our approach is straightforward to implement, does not require any architectural modifications, and simultaneously optimizes weights and ranks filter importance, all without the need for extensive fine-tuning. We validated the efficacy of our method on modern DCNN architectures using the MNIST, CIFAR, and ImageNet datasets, achieving competitive performance compared to existing structured pruning approaches.
Mitigating Relative Over-Generalization in Multi-Agent Reinforcement Learning
Venue: arXiv cs.LG 2024
Year: 2024

In decentralized multi-agent reinforcement learning, agents learning in isolation can lead to relative over-generalization (RO), where optimal joint actions are undervalued in favor of suboptimal ones. This hinders effective coordination in cooperative tasks, as agents tend to choose actions that are individually rational but collectively suboptimal. To address this issue, we introduce MaxMax Q-Learning (MMQ), which employs an iterative process of sampling and evaluating potential next states, selecting those with maximal Q-values for learning. This approach refines approximations of ideal state transitions, aligning more closely with the optimal joint policy of collaborating agents. We provide theoretical analysis supporting MMQ's potential and present empirical evaluations across various environments susceptible to RO. Our results demonstrate that MMQ frequently outperforms existing baselines, exhibiting enhanced convergence and sample efficiency.
Learning the Sherrington-Kirkpatrick Model Even at Low Temperature
Venue: arXiv cs.LG 2024
Year: 2024

We consider the fundamental problem of learning the parameters of an undirected graphical model or Markov Random Field (MRF) in the setting where the edge weights are chosen at random. For Ising models, we show that a multiplicative-weight update algorithm due to Klivans and Meka learns the parameters in polynomial time for any inverse temperature $β\leq \sqrt{\log n}$. This immediately yields an algorithm for learning the Sherrington-Kirkpatrick (SK) model beyond the high-temperature regime of $β< 1$. Prior work breaks down at $β= 1$ and requires heavy machinery from statistical physics or functional inequalities. In contrast, our analysis is relatively simple and uses only subgaussian concentration. Our results extend to MRFs of higher order (such as pure $p$-spin models), where even results in the high-temperature regime were not known.
Making Sigmoid-MSE Great Again: Output Reset Challenges Softmax Cross-Entropy in Neural Network Classification
Venue: arXiv cs.LG 2024
Year: 2024

This study presents a comparative analysis of two objective functions, Mean Squared Error (MSE) and Softmax Cross-Entropy (SCE) for neural network classification tasks. While SCE combined with softmax activation is the conventional choice for transforming network outputs into class probabilities, we explore an alternative approach using MSE with sigmoid activation. We introduce the Output Reset algorithm, which reduces inconsistent errors and enhances classifier robustness. Through extensive experiments on benchmark datasets (MNIST, CIFAR-10, and Fashion-MNIST), we demonstrate that MSE with sigmoid activation achieves comparable accuracy and convergence rates to SCE, while exhibiting superior performance in scenarios with noisy data. Our findings indicate that MSE, despite its traditional association with regression tasks, serves as a viable alternative for classification problems, challenging conventional wisdom about neural network training strategies.
Don't Be So Positive: Negative Step Sizes in Second-Order Methods
Venue: arXiv cs.LG 2024
Year: 2024

The value of second-order methods lies in the use of curvature information. Yet, this information is costly to extract and once obtained, valuable negative curvature information is often discarded so that the method is globally convergent. This limits the effectiveness of second-order methods in modern machine learning. In this paper, we show that second-order and second-order-like methods are promising optimizers for neural networks provided that we add one ingredient: negative step sizes. We show that under very general conditions, methods that produce ascent directions are globally convergent when combined with a Wolfe line search that allows both positive and negative step sizes. We experimentally demonstrate that using negative step sizes is often more effective than common Hessian modification methods.